Pharmacometric Modeling Strategies

Under construction

Alternatives in PKPD modelling

  • Assume a PK model
    • Estimate PK and PD parameters in a simultaneous fit (SIM)
    • Estimate PK parameters first and then fit PD
      • Condition on individual PK parameter estimates
        • Assume no error in parameters (IPP = Individual PK Parameters)
        • Account for error in parameters (IPPSE = Individual PK Parameters Standard Error)*
      • Fix population PK parameters
        • Include individual PK data (PPP&D = Population PK Parameters & Data)**
        • Don’t include individual PK data (PPP = Population PK Parameters)

*LaCroix et al., JPKPD 39:177–193, 2012
**Wade and Karlsson, PAGE 1999
Abbreviations in Zhang et al 2003

Important considerations

Impact of a model

  • What is the modeling used for? (e.g., bridging, dose, SmPC1 parameters?)
    • Does the conclusion align with the aim?
  • What data is available?
    • Rich data
    • Sparse data
  • What is the structural model?
    • Reasonable parameter estimates and RSE2’s?
    • Graphical evaluation (VPC3 first)
    • Covariate evaluation
  • Exposure-response is generally non-informative if only one dose-level is given, even if weight-adjusted

Reviewing models

  • Does my conclusion align with the authors?
  • Questions NGN (eNGiNe)
    • Need-to-know: Will affect conclusion (Major objection)
    • Good-to-know: Could affect conclusion (Other concern)
    • Nice-to-know: Won’t affect conclusion (avoid asking this question)

References

  • Musuamba et al., 2021, https://doi.org/10.1002/psp4.12669
  • Skottheim Rusten & Musuamba, 2021, https://doi.org/10.1002/psp4.12708

Footnotes

  1. Summary of Product Characteristics↩︎

  2. Relative standard error↩︎

  3. Visual Predictive Check↩︎